GOOML: Geothermal Operational Optimization with Machine Learning

Jon Weers, Paul Siratovich, Andrea Blair

Research output: Contribution to conferencePaperpeer-review

1 Scopus Citations


Geothermal Operational Optimization with Machine Learning (GOOML) is a project focused on maximizing increased availability and capacity from existing industrial-scale geothermal generation assets. The GOOML project will develop a suite of machine learning-based algorithms that analyze historical production datasets and provide predictive setpoints for geothermal field operations. Historical datasets from New Zealand and the US will provide the input to develop digital geothermal system twins which allow prediction of market conditions, maintenance operations and steamfield optimization. The algorithms will identify key parameters within fields and suggest setpoints for components of the system to maintain optimal generation. Set-points can be instructed to follow mass flow restrictions, generation maximization and optimal field/reservoir balance and give field operators a guide by which generation can be optimized. The datasets that will be used to develop GOOML are sourced from operating geothermal fields in New Zealand and the United States with varying degrees of complexity. This will ensure that most geothermal systems can utilize the GOOML tool to assist in optimizing operations. GOOML aims to achieve a step-change in geothermal operations by developing state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind automated, intelligent geothermal system model.

Original languageAmerican English
Number of pages10
StatePublished - 2020
EventGeothermal Resources Council Virtual Annual Meeting and Expo: Clean, Renewable and Always On, GRC 2020 - Virtual, Online
Duration: 19 Oct 202023 Oct 2020


ConferenceGeothermal Resources Council Virtual Annual Meeting and Expo: Clean, Renewable and Always On, GRC 2020
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2020 Geothermal Resources Council. All rights reserved.

NREL Publication Number

  • NREL/CP-6A20-79795


  • Algorithms
  • Big data
  • Field optimization
  • Machine learning


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